Data Science and Machine Learning with R - Data Preprocessing Introduction
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Information Technology (IT), Architecture
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University
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Practice Problem
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Hard
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is data preprocessing considered crucial in machine learning?
It eliminates the need for data splitting.
It ensures the data is clean and organized for modeling.
It simplifies the algorithms used.
It reduces the need for feature engineering.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary purpose of using tidy models in R?
To eliminate the need for data preprocessing.
To make R compatible with Python.
To unify various functions into a single framework.
To replace all other R packages.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a common issue with real-world data that necessitates preprocessing?
It is always ready for machine learning models.
It is always in a numerical format.
It often contains errors and inconsistencies.
It is always perfectly structured.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why should data be split into training and testing sets before preprocessing?
To ensure the model is trained on all available data.
To simplify the data cleaning process.
To validate the preprocessing steps and model objectively.
To avoid the need for feature engineering.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the risk of using the testing data multiple times during model development?
It biases the model towards the testing data.
It improves the model's accuracy.
It eliminates the need for cross-validation.
It simplifies the preprocessing steps.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is feature engineering in the context of data preprocessing?
The elimination of the need for data splitting.
The process of removing all features from a dataset.
The process of converting numerical data to categorical data.
The creation or transformation of features to improve model performance.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is a method to handle missing values in a dataset?
Ignoring them completely.
Scaling them to a standard range.
Using imputation techniques like mean or median.
Converting them to categorical data.
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